\section{Conclusion}
	
	Firstly, this paper has examined the Lyngsoe Systems BagTrack database schema, briefly discussing the implications of using this dataset in a data warehouse.
	
	Next, a data warehouse model has been proposed, based on a discussion of relevant schema templates. The chosen dimensions were defined and justified, as well as the fact table and the measures it contains. Furthermore, the suggested aggregation functions to be used with the measures were shortly explained.
	
	Our Extract-Transform-Load (ETL) tool was then detailed, examining some of the problems that arose with the BagTrack dataset with regard to importing the data into our data warehouse. The transformation algorithm is outlined, and its steps explained.
	
	Finally, the evaluation reflected upon some of our decisions about the modelling of the data warehouse schema, shortly considering performance implications of these decisions with positive outcome. Furthermore, the impact of anomalous raw readings on the data transformation algorithm used by the ETL tool is discussed, with the result that certain error types can be tolerated, namely redundant readings by the same reader, while others are very harmful to the system, namely bags being erroneously read by readers in the wrong locations.
	
	In conclusion, the model is fit for real-world implementation, provided a data cleansing method can be devised in order to eliminate the erroneous readings. Further improvements to the BagTrack system, such as readers that detect when a bag is being loaded onto an airplane, and more detailed knowledge about the layouts of the airport baggage sorting systems, would allow for great improvements of the warehouse with regard to detecting baggage being lost, and troubleshooting the sorting systems in order to improve their quality.